thetis
Service to examine data processing pipelines (e.g., machine learning or deep learning pipelines) for uncertainty consistency (calibration), fairness, and other safety-relevant aspects.
hierarchical-dnn-interpretations
Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
azimuth
Helping AI practitioners better understand their datasets and models in text classification. From ServiceNow.
talktomodel
TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!
modeling-uncertainty-local-explainability
Local explanations with uncertainty 💐!
3d-viz-score-cam
Visualizing 3D ResNet for Medical Image Classification With Score-CAM
awesome-safety-critical-ai
When the stakes are high, intelligence is only half the equation - reliability is the other ⚠️
surrogates-tutorial
What and How of Machine Learning Transparency – ECML-PKDD 2020 Hands-on Tutorial
model-guidance
Code for the paper: Studying How to Efficiently and Effectively Guide Models with Explanations. ICCV 2023.
awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
machine-learning-neural-python
Introduction to artificial neural nets with Python